Latent functional connectivity underlying multiple brain states
AbstractFunctional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to reflect the brain’s intrinsic network architecture; thought to be broadly relevant because it persists across brain states (i.e., state-general). However, i...
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| Format: | Article |
| Language: | English |
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The MIT Press
2022-01-01
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| Series: | Network Neuroscience |
| Online Access: | https://direct.mit.edu/netn/article/doi/10.1162/netn_a_00234/109243/Latent-functional-connectivity-underlying-multiple |
| _version_ | 1819274638870446080 |
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| author | Ethan M. McCormick Katelyn L. Arnemann Takuya Ito Stephen José Hanson Michael W. Cole |
| author_facet | Ethan M. McCormick Katelyn L. Arnemann Takuya Ito Stephen José Hanson Michael W. Cole |
| author_sort | Ethan M. McCormick |
| collection | DOAJ |
| description |
AbstractFunctional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to reflect the brain’s intrinsic network architecture; thought to be broadly relevant because it persists across brain states (i.e., state-general). However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting shared connectivity patterns across many brain states, better captures state-general intrinsic FC relative to measures derived from resting state alone. We estimated latent FC independently for each connection using leave-one-task-out factor analysis in 7 highly distinct task states (24 conditions) and resting state using fMRI data from the Human Connectome Project. Compared to resting-state connectivity, latent FC improves generalization to held-out brain states, better explaining patterns of connectivity and task-evoked activation. We also found that latent connectivity improved prediction of behavior outside the scanner, indexed by the general intelligence factor (g). Our results suggest that FC patterns shared across many brain states, rather than just resting state, better reflects state-general connectivity. This affirms the notion of “intrinsic” brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor. |
| first_indexed | 2024-12-23T23:11:37Z |
| format | Article |
| id | doaj.art-a6443e539fb6460b908bbe1c11043540 |
| institution | Directory Open Access Journal |
| issn | 2472-1751 |
| language | English |
| last_indexed | 2024-12-23T23:11:37Z |
| publishDate | 2022-01-01 |
| publisher | The MIT Press |
| record_format | Article |
| series | Network Neuroscience |
| spelling | doaj.art-a6443e539fb6460b908bbe1c110435402022-12-21T17:26:39ZengThe MIT PressNetwork Neuroscience2472-17512022-01-0114210.1162/netn_a_00234Latent functional connectivity underlying multiple brain statesEthan M. McCormick0http://orcid.org/0000-0002-7919-4340Katelyn L. Arnemann1http://orcid.org/0000-0003-0454-0592Takuya Ito2http://orcid.org/0000-0002-2060-4608Stephen José Hanson3http://orcid.org/0000-0003-1985-2054Michael W. Cole4http://orcid.org/0000-0003-4329-438XCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United StatesCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United StatesCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United StatesRutgers University Brain Imaging Center, Newark, New Jersey, United StatesCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States AbstractFunctional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to reflect the brain’s intrinsic network architecture; thought to be broadly relevant because it persists across brain states (i.e., state-general). However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting shared connectivity patterns across many brain states, better captures state-general intrinsic FC relative to measures derived from resting state alone. We estimated latent FC independently for each connection using leave-one-task-out factor analysis in 7 highly distinct task states (24 conditions) and resting state using fMRI data from the Human Connectome Project. Compared to resting-state connectivity, latent FC improves generalization to held-out brain states, better explaining patterns of connectivity and task-evoked activation. We also found that latent connectivity improved prediction of behavior outside the scanner, indexed by the general intelligence factor (g). Our results suggest that FC patterns shared across many brain states, rather than just resting state, better reflects state-general connectivity. This affirms the notion of “intrinsic” brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.https://direct.mit.edu/netn/article/doi/10.1162/netn_a_00234/109243/Latent-functional-connectivity-underlying-multiple |
| spellingShingle | Ethan M. McCormick Katelyn L. Arnemann Takuya Ito Stephen José Hanson Michael W. Cole Latent functional connectivity underlying multiple brain states Network Neuroscience |
| title | Latent functional connectivity underlying multiple brain states |
| title_full | Latent functional connectivity underlying multiple brain states |
| title_fullStr | Latent functional connectivity underlying multiple brain states |
| title_full_unstemmed | Latent functional connectivity underlying multiple brain states |
| title_short | Latent functional connectivity underlying multiple brain states |
| title_sort | latent functional connectivity underlying multiple brain states |
| url | https://direct.mit.edu/netn/article/doi/10.1162/netn_a_00234/109243/Latent-functional-connectivity-underlying-multiple |
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